Computer Science > Computation and Language

Title:
Recurrent Dropout without Memory Loss

Abstract: This paper presents a novel approach to recurrent neural network (RNN)
regularization. Differently from the widely adopted dropout method, which is
applied to \textit{forward} connections of feed-forward architectures or RNNs,
we propose to drop neurons directly in \textit{recurrent} connections in a way
that does not cause loss of long-term memory. Our approach is as easy to
implement and apply as the regular feed-forward dropout and we demonstrate its
effectiveness for Long Short-Term Memory network, the most popular type of RNN
cells. Our experiments on NLP benchmarks show consistent improvements even when
combined with conventional feed-forward dropout.